code: def templatePredict(
      path_to_file,
      initial_medoids,
      points,
      expected_closest_clusters,
      ccore,
      **kwargs,
  ):
      sample = read_sample(path_to_file)
  
      metric = kwargs.get(
          "metric", distance_metric(type_metric.EUCLIDEAN_SQUARE)
      )
      itermax = kwargs.get("itermax", 200)
  
      kmedoids_instance = kmedoids(
          sample,
          initial_medoids,
          0.001,
          ccore,
          metric=metric,
          itermax=itermax,
      )
      kmedoids_instance.process()
  
      closest_clusters = kmedoids_instance.predict(points)
      assertion.eq(
          len(expected_closest_clusters), len(closest_clusters)
      )
      assertion.true(
          numpy.array_equal(
              numpy.array(expected_closest_clusters), closest_clusters
          )
      )
  
file_path: pyclustering/pyclustering/cluster/tests/kmedoids_templates.py
imports: import math
  import numpy
  from pyclustering.tests.assertion import assertion
  from pyclustering.cluster.kmedoids import kmedoids, build
  from pyclustering.cluster.center_initializer import kmeans_plusplus_initializer
  from pyclustering.samples import answer_reader
  from pyclustering.utils import read_sample, calculate_distance_matrix
  from pyclustering.utils.metric import distance_metric, type_metric
  from random import random, randint
label_desc: Use image node_id 6 for calling the kmedoids_test_template obj's underlying member method code with example usage: obj.templatePredict(path_to_file, initial_medoids, points, expected_closest_clusters, ccore) without return types